{"as_of":"2026-08-09T08:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b533ebf8cf0f1cedf353f0da78b92ab9949c111a4992372d05c5fd5e8e798c92","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T19:53:26.173725Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.05987/citation-record","integrity":"/paper/2608.05987/integrity","json":"/paper/2608.05987/citation-record.json","paper":"/paper/2608.05987"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:24.992869Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:24.992869Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:8c6b30caf3cd96aecd08dfd4974ab34b5781d150585d7df1b13a0defb3f4604f","observation_id":"82d516c7-7361-4464-8e74-9c0c1715ae86","resolution":{"observed_at":"2026-08-07T19:53:24.992869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:24.998800Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:24.998800Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:5bb25057171372bc40714ee7a181e0d3ce018cbf8de9179ff03516692b7d18cb","observation_id":"9b876981-78b5-410b-af7a-3a85d061131b","resolution":{"observed_at":"2026-08-07T19:53:24.998800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.004243Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.004243Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:ea777c4f97512fc259f3dbdfe698ee9279aa00f74a7d567d79e9cf361f0ec69a","observation_id":"942e9632-ec05-48bf-9c02-77f51e92b41e","resolution":{"observed_at":"2026-08-07T19:53:25.004243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.009238Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.009238Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:30865b7d91ecea2f23b536f3ca30292db60e13c7d2c5b4135bda8f41661ba021","observation_id":"39cb9bad-9b74-4640-8722-3fa2f343bdf6","resolution":{"observed_at":"2026-08-07T19:53:25.009238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.034126Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.034126Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:6f5ff88df83b84ceee57cfaf0cf748f3fc564efe0b96936961dc0acb71eca629","observation_id":"667a98ec-614e-47d4-8999-26fb44de5b99","resolution":{"observed_at":"2026-08-07T19:53:25.034126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:29.108382Z","title":"2026 , eprint=","venue":null,"work_id":"d8661418-a1f0-4634-87ec-886ee1538790","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.085810Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:bd5f8280f291ea293d99079ec74d4d58238eb41f8c414646f1054bbe2f698694","observation_id":"7d3d36e5-cd90-433b-bacb-6f821ec383a5","resolution":{"observed_at":"2026-08-07T19:53:29.145173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.115986Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.115986Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:cd30b5bfa9c792bfbb605ba61260ad6ea63bb39820da35d53c653f98d5a107a7","observation_id":"38f9310c-887e-4e49-82dc-a9f20ac46ec1","resolution":{"observed_at":"2026-08-07T19:53:25.115986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.149185Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.149185Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:b71b6b99577915c1aaf2ab129cae8c0bc554aecdc9f4de6e93ee052dbbab76e0","observation_id":"e7c5d4b0-11bd-4d90-8efc-386bb35b325f","resolution":{"observed_at":"2026-08-07T19:53:25.149185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.176942Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.176942Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:a7e053586fbc47b05833ba23e78f3dcac3b3ff4c98591aceb3f2de841426311c","observation_id":"b38d946d-e347-49ed-9f92-f7c6a045cff9","resolution":{"observed_at":"2026-08-07T19:53:25.176942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.189302Z","title":"The eleventh international conference on learning representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.189302Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:4937f662157287c4f158a7a0f49279ee4468957d294fb532c97bed7963b02a07","observation_id":"f6aa2346-0797-4f1c-b7eb-0d3b0690e0f3","resolution":{"observed_at":"2026-08-07T19:53:25.189302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03768","last_updated":"2021-03-14T22:44:38Z","snapshot_observed_at":"2026-08-08T17:40:47.037804Z","submitted_at":"2020-10-08T05:13:36Z","title":"ALFWorld: Aligning Text and Embodied Environments for Interactive Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.03768","snapshot_observed_at":"2026-08-07T19:53:25.218055Z","title":"arXiv preprint arXiv:2010.03768 , year=","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.218055Z"},"links":{"cited_paper":"/paper/2010.03768","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:d5a4a135144fa6c313e8194a0826eacc4b324e98cbbfe4b16fd2c9875f80770a","observation_id":"46fbc3ad-f81c-4ce0-a59e-7e629cc2bcbf","resolution":{"observed_at":"2026-08-07T19:53:25.218055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.09516","last_updated":"2025-08-05T19:08:38Z","snapshot_observed_at":"2026-07-06T20:51:28.022519Z","submitted_at":"2025-03-12T16:26:39Z","title":"Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.09516","snapshot_observed_at":"2026-08-07T19:53:25.273982Z","title":"arXiv preprint arXiv:2503.09516 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.273982Z"},"links":{"cited_paper":"/paper/2503.09516","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:b56c9c3f597a58d8ffef212d696819d7241f614fdf1d71e972543a8dbc92389c","observation_id":"93c6b923-0541-4cbf-8dfc-cf2d80c5f27a","resolution":{"observed_at":"2026-08-07T19:53:25.273982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.325014Z","title":"Transactions of the Association for Computational Linguistics , volume=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.325014Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:7d5c171fb235df45a431475244ef25cab8ad0a5f43413f3fe042ffa6867c4e42","observation_id":"80ccb4e7-5879-4ca6-8af8-86c1b2414914","resolution":{"observed_at":"2026-08-07T19:53:25.325014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.341911Z","title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.341911Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:af2ca77e4ab68445f48f5d6a744f7a096810ff4d6545af742a92a41392d83071","observation_id":"05607002-a70e-44f7-bb3e-313b32897772","resolution":{"observed_at":"2026-08-07T19:53:25.341911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.354446Z","title":"Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.354446Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:7a9c903e2885d3bffb03fcc7bd05797f3c757f10d4d8db97f3635da25b79c006","observation_id":"9f802145-6ca0-4ede-bc71-aefee01ef16d","resolution":{"observed_at":"2026-08-07T19:53:25.354446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.360089Z","title":"Proceedings of the 2018 conference on empirical methods in natural language processing , pages=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.360089Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:7e6468372da7375b027996332bf0c8ceb721ae200872cd71f36a11a96e70efad","observation_id":"baa8d1cd-9425-4610-b82a-e6bbd84bca61","resolution":{"observed_at":"2026-08-07T19:53:25.360089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.366573Z","title":"Proceedings of the 28th International Conference on Computational Linguistics , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.366573Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:c62c7b7158839d617e8e94d105afc6e3c082d3ddfa59e2233b25d3f2d5db4a91","observation_id":"e5af50b8-21fb-4128-b71b-c30f7aa8743c","resolution":{"observed_at":"2026-08-07T19:53:25.366573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.376814Z","title":"Transactions of the Association for Computational Linguistics , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.376814Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:b5638a8f9f9ca4892a96eccfaf0c43ccc7c8ddfd9fc31a418832b47589792bee","observation_id":"38266335-f39a-499d-9c1c-93407a11ae7a","resolution":{"observed_at":"2026-08-07T19:53:25.376814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.391568Z","title":"Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.391568Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:50dc4ce5f9ced30d4a1ea87ab8bb0a5fcc4a6444833e947837c025ff9fdc926b","observation_id":"47c397c8-2827-4ea1-97a6-b37411405097","resolution":{"observed_at":"2026-08-07T19:53:25.391568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10978","last_updated":"2025-10-28T15:11:36Z","snapshot_observed_at":"2026-07-29T19:20:21.974239Z","submitted_at":"2025-05-16T08:26:59Z","title":"Group-in-Group Policy Optimization for LLM Agent Training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.10978","snapshot_observed_at":"2026-08-07T19:53:25.400799Z","title":"arXiv preprint arXiv:2505.10978 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.400799Z"},"links":{"cited_paper":"/paper/2505.10978","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:ee0f680ee52bfe38b7596255baf1cd8b41e217aa63a89cfd650d83a94d05ed99","observation_id":"59f97e98-7067-418a-9b87-e5456bdee721","resolution":{"observed_at":"2026-08-07T19:53:25.400799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.03533","last_updated":"2024-02-22T06:21:51Z","snapshot_observed_at":"2026-07-06T14:27:46.217000Z","submitted_at":"2022-12-07T09:25:54Z","title":"Text Embeddings by Weakly-Supervised Contrastive Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.03533","snapshot_observed_at":"2026-08-07T19:53:25.407335Z","title":"arXiv preprint arXiv:2212.03533 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.407335Z"},"links":{"cited_paper":"/paper/2212.03533","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:0895c59c198ee5873e9b3dcff72df5a69a5790c1a18ca3780d0c469358166317","observation_id":"0799257c-2fd8-465b-8d62-1b94be6143c5","resolution":{"observed_at":"2026-08-07T19:53:25.407335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T19:53:25.413689Z","title":"arXiv preprint arXiv:2501.12948 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.413689Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:f1293d659fa8eea0f8bb9466e02b42e765d72e6620aff4e966f84f132c8d1d13","observation_id":"e19984f0-4bd1-49d2-a2f6-1f328b391069","resolution":{"observed_at":"2026-08-07T19:53:25.413689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-07T19:53:25.418872Z","title":"arXiv preprint arXiv:2402.03300 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.418872Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:3ebb9a1dec78c776fbf399b6461f1ef7d0a868fb8c14cde0b0c857d5a73c5410","observation_id":"19a84209-762e-4208-8078-1552acbeca38","resolution":{"observed_at":"2026-08-07T19:53:25.418872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.424814Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.424814Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:ab6b9aefa046f060fdcbed6ce2052e9f50c0dd9d4a9293ed2b686b998d5a6cc5","observation_id":"ed2c257f-72f6-4040-8532-4305bb8a4b74","resolution":{"observed_at":"2026-08-07T19:53:25.424814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.935055Z","title":null,"venue":null,"work_id":"a9c31a73-047b-4f07-bb74-33a5a7989f84","year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.429457Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:d7a88e99f0abe8b92e9698d7924ad9c1b9f48c8f3528c1f5d455ad4c52ec3685","observation_id":"435bca4a-1b8b-4bbf-babb-8c9198a3daf4","resolution":{"observed_at":"2026-08-07T19:53:28.960790Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-08-07T19:53:25.434211Z","title":"arXiv preprint arXiv:2505.09388 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.434211Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:014c2939a1bebdbeb58b0b16b8dfedb73521f8a00eb9c2a53026764dd920090d","observation_id":"9b77dde9-97cb-4019-9b5b-082451aeb98a","resolution":{"observed_at":"2026-08-07T19:53:25.434211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.20534","last_updated":"2026-02-03T04:57:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-28T05:35:43Z","title":"Kimi K2: Open Agentic Intelligence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.20534","snapshot_observed_at":"2026-08-07T19:53:25.438567Z","title":"arXiv preprint arXiv:2507.20534 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.438567Z"},"links":{"cited_paper":"/paper/2507.20534","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:4c3d89659c73345bc17b5f7cd4304f053fce73b91a0c6116f18d7f4a9003f76f","observation_id":"d481ff67-37a9-46e6-9107-65384b2c8bc8","resolution":{"observed_at":"2026-08-07T19:53:25.438567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.879025Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":"41a6ec35-b556-41cd-b21c-0092a8f496cf","year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.444028Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:f7753107699d141446cf8a16b4688cf3c1a4010fadd951f8b405a0df90eccce4","observation_id":"3763a7ad-38f7-4568-ae6b-fe66c08fd348","resolution":{"observed_at":"2026-08-07T19:53:28.902064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.816285Z","title":"Proceedings of the ACM on Web Conference 2025 , pages=","venue":null,"work_id":"996b5003-c87a-4fce-beb4-387311667ae1","year":2025},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.449543Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:87db3bc3fb78aa38f73ff3926d35dc7fbe740e21187f0cf3001f5672a1a38f4a","observation_id":"c197bb77-bb0a-48d7-9159-3b7a12768644","resolution":{"observed_at":"2026-08-07T19:53:28.850775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06261","last_updated":"2025-12-19T14:25:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-07T17:36:04Z","title":"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06261","snapshot_observed_at":"2026-08-07T19:53:25.454538Z","title":"arXiv preprint arXiv:2507.06261 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.454538Z"},"links":{"cited_paper":"/paper/2507.06261","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:00d58b749b2cab59c562f9b8c16fd8fd28614b401987328e3243f1039cd91b7a","observation_id":"bbbdf02e-35a0-456f-8d4c-fce5ee880c45","resolution":{"observed_at":"2026-08-07T19:53:25.454538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.459799Z","title":"arXiv preprint arXiv:2601.16725 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.459799Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:f6e1de334091ad17331e844d3392a55d13714e436006b48dc90337461f11a1ac","observation_id":"4862341c-860a-4872-984e-94d3f753b5e5","resolution":{"observed_at":"2026-08-07T19:53:25.459799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-07T19:53:25.464858Z","title":"arXiv preprint arXiv:2410.21276 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.464858Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:1c074885d92ce0c60f7bd31532703be594911fb8c54beab1088b0a0787623890","observation_id":"3057d07f-715f-4706-a0eb-4d67daca02a5","resolution":{"observed_at":"2026-08-07T19:53:25.464858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.470259Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.470259Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:3470f3d644451c139384af442dfba43003ef5160f9f211d83337275d47e1322e","observation_id":"08ba990a-7737-4c8c-828d-c4f80992a5dd","resolution":{"observed_at":"2026-08-07T19:53:25.470259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06770","last_updated":"2024-11-11T23:05:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T16:47:29Z","title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06770","snapshot_observed_at":"2026-08-07T19:53:25.475258Z","title":"arXiv preprint arXiv:2310.06770 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.475258Z"},"links":{"cited_paper":"/paper/2310.06770","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:52ec89c5a304cb955bcb89a492832cdaab8fb3c8390317286a5c00c5dcafbb38","observation_id":"1e755faf-f6ac-40fe-96e7-97639f0b215f","resolution":{"observed_at":"2026-08-07T19:53:25.475258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.19849","last_updated":"2025-07-26T07:53:11Z","snapshot_observed_at":"2026-07-06T22:03:15.296567Z","submitted_at":"2025-07-26T07:53:11Z","title":"Agentic Reinforced Policy Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.19849","snapshot_observed_at":"2026-08-07T19:53:25.480329Z","title":"arXiv preprint arXiv:2507.19849 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.480329Z"},"links":{"cited_paper":"/paper/2507.19849","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:4048510d0760212c0455f9d7ea808ece61e3b93f2c1522f1ac6b31fb68409e79","observation_id":"dc2c9a41-6e4e-4169-98b9-6e3b1d2631e2","resolution":{"observed_at":"2026-08-07T19:53:25.480329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.484747Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.484747Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:26160bca44a5679be9cbfc9fc002ea94ac5980dbdc7d2807cf3ec0a4274e6a30","observation_id":"e08336bb-95f8-45eb-ac97-0cc0c28fcf84","resolution":{"observed_at":"2026-08-07T19:53:25.484747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.511208Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.511208Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:c0674375ba99a9d9f51fd697e7ac6d5800af2c59bf89b1ad56d86ad898d09f3c","observation_id":"572c8c8f-2052-4491-9a37-6cd4287f1e2d","resolution":{"observed_at":"2026-08-07T19:53:25.511208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.549779Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.549779Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:90e87a467b3a8367bbe333380ae2d196cc35594f95ca329bc9a3f1b7b960d78e","observation_id":"eafcfba5-3be3-4aa8-b6e1-d2ba35632447","resolution":{"observed_at":"2026-08-07T19:53:25.549779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.564156Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.564156Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:6dec7f12d511f5407599a10b5d5a66a538d21daaf9db8b0a2eaa467b2cd1a8d6","observation_id":"0782cfde-7851-4e2e-ac2c-4037575d4ff3","resolution":{"observed_at":"2026-08-07T19:53:25.564156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.595205Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.595205Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:072e1a1e6ebf42e2f1200b72e95c09a1e3165fb9417230b459c24708f06d4e22","observation_id":"8afca331-ee0c-4835-bc1b-a594829a9ecc","resolution":{"observed_at":"2026-08-07T19:53:25.595205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.571676Z","title":"2026 , eprint=","venue":null,"work_id":"1e87d5a9-527b-4d78-879f-66998feda13e","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.623667Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:6d9818562c6321e0ee80072c42d477b5cabdd962d15f855d91a0673e68092763","observation_id":"6655eeed-dbc9-4b51-b32e-426f2c580a98","resolution":{"observed_at":"2026-08-07T19:53:28.619108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.647136Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.647136Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:c3d2d412d6dcbde1c8ebb49be809f974098dfbba168ab89987a2c8d918e1182a","observation_id":"d506fd67-ed4c-452e-9700-251ccf57922a","resolution":{"observed_at":"2026-08-07T19:53:25.647136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.671107Z","title":"2011 , eprint=","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.671107Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:d8fc4573f5797c5bff0cd72c363eb3b1c97c2459291d87e5a12fbbb858ccb7c2","observation_id":"0694b948-1b45-429c-b819-511adca85e8b","resolution":{"observed_at":"2026-08-07T19:53:25.671107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.719544Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.719544Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:c2c20da25928403c6d1467e64ee5633435e4bed1b9b95576a624b945cd9b484d","observation_id":"9d9835cf-a47d-46c9-a800-00dddecdce5e","resolution":{"observed_at":"2026-08-07T19:53:25.719544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.420425Z","title":"2019 , eprint=","venue":null,"work_id":"088c8c5d-11ed-488a-9cae-d6e04505dafc","year":2019},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.744472Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:c847269be11324dd82e3c007bbe00a25237199a9e0479d164630fa132eb53ed8","observation_id":"51d3c44d-c768-4c71-8091-f0dc1852d21d","resolution":{"observed_at":"2026-08-07T19:53:28.481273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.15144","last_updated":"2025-09-01T05:38:34Z","snapshot_observed_at":"2026-07-06T22:15:55.690108Z","submitted_at":"2025-08-21T00:39:12Z","title":"Mobile-Agent-v3: Fundamental Agents for GUI Automation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.15144","snapshot_observed_at":"2026-08-07T19:53:25.765585Z","title":"arXiv preprint arXiv:2508.15144 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.765585Z"},"links":{"cited_paper":"/paper/2508.15144","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:eb5b03fbf2d54bb123d749839026819c126b93accd2da33d721ae05e90a886db","observation_id":"26c3af54-464c-4908-9818-21de4473cd16","resolution":{"observed_at":"2026-08-07T19:53:25.765585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16291","last_updated":"2023-10-19T16:27:03Z","snapshot_observed_at":"2026-08-07T08:29:46.650400Z","submitted_at":"2023-05-25T17:46:38Z","title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16291","snapshot_observed_at":"2026-08-07T19:53:25.786093Z","title":"arXiv preprint arXiv:2305.16291 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.786093Z"},"links":{"cited_paper":"/paper/2305.16291","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:bd2b1fbb0cc7faf7f542988679740bcbb1c732605dac1e9de67e8c1978e634b3","observation_id":"69b7dcf3-89c5-4f58-878c-33c07661a583","resolution":{"observed_at":"2026-08-07T19:53:25.786093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.791433Z","title":"2024 , eprint=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.791433Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:6a044feaad9cccadf66cbb23d9e418848c591f2fd206d661c453a7ae57c103f1","observation_id":"d0db4c0b-5dc5-4c92-861a-cdd6372cfdf0","resolution":{"observed_at":"2026-08-07T19:53:25.791433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.796098Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.796098Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:3584b44d034c52c43bfacb608c4b50bfd64d18504247d768b97efdc2c336cc4c","observation_id":"eee1988e-068f-45b6-b20e-d4002c8314f8","resolution":{"observed_at":"2026-08-07T19:53:25.796098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.800734Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.800734Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:d0373e0712e79c626ac1e47f656f7a055d548ea5c8745b52cb2a18e839fb329a","observation_id":"97c813f3-7da2-420d-a63b-8389b4d8abb0","resolution":{"observed_at":"2026-08-07T19:53:25.800734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14476","last_updated":"2025-05-20T01:37:34Z","snapshot_observed_at":"2026-08-02T01:40:54.187278Z","submitted_at":"2025-03-18T17:49:06Z","title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14476","snapshot_observed_at":"2026-08-07T19:53:25.805370Z","title":"arXiv preprint arXiv:2503.14476 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.805370Z"},"links":{"cited_paper":"/paper/2503.14476","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:221cc9f85a1a829329b039b3037fbbfcd4d4213a9d3e2f87f18e15bbcbafd757","observation_id":"53474c2f-3fdb-4541-b82d-b9b86cbf9833","resolution":{"observed_at":"2026-08-07T19:53:25.805370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.283781Z","title":"2026 , eprint=","venue":null,"work_id":"118f44e9-7f84-42c5-a7bd-1a242c6040ef","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.814977Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:8400065dd21b583162cec8f194c709d1e1002b5f087b2ace19bd68e1039f37da","observation_id":"3236c731-949b-4c7e-9279-ebe740505a87","resolution":{"observed_at":"2026-08-07T19:53:28.317587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.824341Z","title":"arXiv preprint arXiv:2602.03048 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.824341Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:8c9b1209b58e3a6cda67afd03c60fc291b1ad6168b6176ebf51ab8a868bea907","observation_id":"d6c29359-0c29-4e01-a06c-fa5bb5030e1f","resolution":{"observed_at":"2026-08-07T19:53:25.824341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.216894Z","title":"2026 , eprint=","venue":null,"work_id":"1f43bd2e-fdda-4598-b2a4-66152e9bb372","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.837906Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:8f3d6618db2597c90d58c0f9f4edf4eac1008f142c605b2327a36e12ecacb30f","observation_id":"c9e66127-70dc-4a95-9552-615c82320dc8","resolution":{"observed_at":"2026-08-07T19:53:28.237592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.202135Z","title":"2026 , eprint=","venue":null,"work_id":"7af3bc20-a2d9-4713-81db-061f66d8ef7e","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.854963Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:ad5c08ac0369a05b519fbe11b5d043a079e80e5b4fbf0d52817b08f547e65c26","observation_id":"21728072-1869-488f-9096-e1cef7744cfa","resolution":{"observed_at":"2026-08-07T19:53:28.206314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.186101Z","title":"2026 , eprint=","venue":null,"work_id":"e969f7eb-29f6-4de2-b0c8-cfcf12f6fdbb","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.868092Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:c9a30b3f598688ef55cd1556426ba7bb0a5400f13c680dc5a00d6b8d25a66c81","observation_id":"34ea40fb-8d47-4cd8-9d2d-bbc6dac40c63","resolution":{"observed_at":"2026-08-07T19:53:28.190864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.872410Z","title":"2017 , eprint=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.872410Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:0cb444358dd066e60b8254eac6abadd7278e756cd79dcb10743c9ca3c15e97ef","observation_id":"9808c1ba-c370-49b1-9be9-5c1f14521c1d","resolution":{"observed_at":"2026-08-07T19:53:25.872410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.161110Z","title":"2016 , eprint=","venue":null,"work_id":"86476024-a959-47ec-9e5e-a668bfc09bfe","year":2016},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.877195Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:f6fa057165159892144367aad5dba449834d1967383cfc21ef1c705c19c21067","observation_id":"af5e4c2e-d7f3-4cad-b2d3-cda22e9a7338","resolution":{"observed_at":"2026-08-07T19:53:28.165787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.144768Z","title":"2019 , eprint=","venue":null,"work_id":"fcfa00ae-e05a-4835-8e98-a10f9eb44986","year":2019},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.882562Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:a2ce75b0312087bf82b68b67dba06eae0113bfbb5d1510b999e250de563c1466","observation_id":"571504e8-9896-4662-9772-a7c3a091c430","resolution":{"observed_at":"2026-08-07T19:53:28.149599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.129554Z","title":"2024 , eprint=","venue":null,"work_id":"2a486e83-c633-4851-a5c0-7bf2ffbb0b63","year":2024},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.887615Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:9e96568184bd17db1fbf9562313ec2b7def45753c0c78ec1c9ff94453dbc8e38","observation_id":"a7b45576-74ae-44ce-9fa0-e0d633787833","resolution":{"observed_at":"2026-08-07T19:53:28.134605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.892424Z","title":"2025 , eprint=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.892424Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:3746da2e4fcb41be7244708f204d09d9afbb31dc57b5a61139efc2fdf910e1ee","observation_id":"d362eef3-4808-431e-8529-c8335ed30d54","resolution":{"observed_at":"2026-08-07T19:53:25.892424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.102685Z","title":"Journal of the American Statistical Association , volume=","venue":null,"work_id":"81a7f1bf-a359-4472-8780-98cd3707534d","year":1995},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.896700Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:f81c5ab5ac666a9d3c88e57f811adfa2ea6d0fc9c7c3d9da7e5da0a185eb2973","observation_id":"3bc4c876-61cf-4ab1-a78b-c0ae6c7cb9d3","resolution":{"observed_at":"2026-08-07T19:53:28.107894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.085812Z","title":"The Annals of Mathematical Statistics , volume=","venue":null,"work_id":"c7c96046-68e4-48d1-a5ee-134c050eae3b","year":1945},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.901640Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:b57f89f87244f7b0984330faa95f72b8ea115b99902d7d2c368ac6f9d4765534","observation_id":"cbb49228-4cbd-44e1-b1c6-5957ad50e682","resolution":{"observed_at":"2026-08-07T19:53:28.091321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2607.14777","last_updated":"2026-07-16T09:57:18Z","snapshot_observed_at":"2026-08-09T03:27:13.521286Z","submitted_at":"2026-07-16T09:57:18Z","title":"SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.14777","snapshot_observed_at":"2026-08-07T19:53:25.906690Z","title":"arXiv preprint arXiv:2607.14777 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.906690Z"},"links":{"cited_paper":"/paper/2607.14777","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:893956feb14482bef9f475dcd1d95a1c20de4a97e378c4b54804812a8be09763","observation_id":"0c69cbd7-d25b-445e-a4ff-0cc00bd9f76b","resolution":{"observed_at":"2026-08-07T19:53:25.906690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.26790","last_updated":"2026-06-25T09:24:09Z","snapshot_observed_at":"2026-08-07T13:00:04.957958Z","submitted_at":"2026-06-25T09:24:09Z","title":"OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2606.26790","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.26790","snapshot_observed_at":"2026-08-07T19:53:27.082155Z","title":"OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning","venue":"cs.CL","work_id":"51221153-2897-4ec2-9ba6-961032e8237f","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.911804Z"},"links":{"cited_paper":"/paper/2606.26790","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:81da84b1d942bf95e9a6c24f52b721d77a5182f520df67ba069c7a946d50a90e","observation_id":"f139449b-87b8-44c3-8130-b16a48d37b81","resolution":{"observed_at":"2026-08-07T19:53:27.087135Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2607.26784","last_updated":"2026-07-29T11:26:33Z","snapshot_observed_at":"2026-08-09T01:01:14.362201Z","submitted_at":"2026-07-29T11:26:33Z","title":"SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution","version":1},"cited_work":{"arxiv_id":"2607.26784","doi":null,"metadata_source":"pith","pith_arxiv_id":"2607.26784","snapshot_observed_at":"2026-08-07T19:53:27.058819Z","title":"SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution","venue":"cs.LG","work_id":"7d8a8639-bc40-47f0-97c3-c76175a6dc2f","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.916475Z"},"links":{"cited_paper":"/paper/2607.26784","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:9123960b095ef70b1c2beecc77432305f3a869dd6daeb41cb966cd3efdc7cc53","observation_id":"711881f7-a651-4888-8f97-894bf50f5a8a","resolution":{"observed_at":"2026-08-07T19:53:27.063688Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.15155","last_updated":"2026-05-14T17:51:26Z","snapshot_observed_at":"2026-08-06T01:14:44.790846Z","submitted_at":"2026-05-14T17:51:26Z","title":"Self-Distilled Agentic Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.15155","snapshot_observed_at":"2026-08-07T19:53:25.921007Z","title":"arXiv preprint arXiv:2605.15155 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.921007Z"},"links":{"cited_paper":"/paper/2605.15155","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:5682bf02d522ecadce21f07b15f05a613353a56df92ac4ddd4a17a441e707316","observation_id":"2822f740-672d-41cc-8588-6f1dfa8c2170","resolution":{"observed_at":"2026-08-07T19:53:25.921007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.926059Z","title":"Artificial Intelligence , volume =","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.926059Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:28ba3127bc4cafdef5e531e20b4d4444bf8d73ca175eef4a28d6b92a5fadbabe","observation_id":"fe8762e6-3bb4-47ce-b6a6-1854bb9d4d11","resolution":{"observed_at":"2026-08-07T19:53:25.926059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:28.044426Z","title":"Journal of Mathematical Analysis and Applications , volume =","venue":null,"work_id":"cade9150-ad51-4be2-a6ee-5e7fe8868a83","year":1965},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.930651Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:04a49a588cea175e9d837e1579647dd89d2b93e2c062f8a2f6a10d417e4f2188","observation_id":"320f4540-0391-40a4-9ae5-48be7875b92c","resolution":{"observed_at":"2026-08-07T19:53:28.062099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.935615Z","title":"arXiv preprint arXiv:2602.07594 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.935615Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:8ab5dc7a16fc4270063e848097ad4264adb8821d1c1c6b57ed549ac202f2baa1","observation_id":"9a0b8028-fd40-4eb3-8fe3-a26b2292cfc6","resolution":{"observed_at":"2026-08-07T19:53:25.935615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.16143","last_updated":"2026-05-15T16:24:16Z","snapshot_observed_at":"2026-08-06T06:27:50.601885Z","submitted_at":"2026-05-15T16:24:16Z","title":"Look Before You Leap: Autonomous Exploration for LLM Agents","version":1},"cited_work":{"arxiv_id":"2605.16143","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.16143","snapshot_observed_at":"2026-08-07T19:53:26.802174Z","title":"Look Before You Leap: Autonomous Exploration for LLM Agents","venue":"cs.AI","work_id":"560866e4-6cbb-474a-a88a-103295f070f5","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.940186Z"},"links":{"cited_paper":"/paper/2605.16143","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:28ada4575104876ddba1293790b04f07b4fa231325d3ade5807b1178ead59e30","observation_id":"fe176c02-ad4a-4efa-b45b-4c78bc7b7dff","resolution":{"observed_at":"2026-08-07T19:53:26.818924Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:25.959586Z","title":"arXiv preprint arXiv:2601.14050 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.959586Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:eb01ae42c950c670dd16871d4a9b3cda601bc1edd990e286354c5b22075a3c65","observation_id":"c41e9e68-7cc8-458d-8f78-953acbafccda","resolution":{"observed_at":"2026-08-07T19:53:25.959586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.24846","last_updated":"2026-05-27T17:45:10Z","snapshot_observed_at":"2026-08-07T09:27:20.671708Z","submitted_at":"2026-05-24T03:31:07Z","title":"Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts","version":2},"cited_work":{"arxiv_id":"2605.24846","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.24846","snapshot_observed_at":"2026-08-07T19:53:26.497300Z","title":"Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts","venue":"cs.LG","work_id":"aa7e88ea-2bc4-4795-bb1d-a246ce66a57b","year":2026},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:25.999733Z"},"links":{"cited_paper":"/paper/2605.24846","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:913e946520ee6c137f966a6df252a4cc9e94d2dfddfcecd9e1cd0185773df082","observation_id":"a1c9e453-598e-4be7-979c-de5c7e49473d","resolution":{"observed_at":"2026-08-07T19:53:26.507641Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.16153","last_updated":"2025-08-25T13:32:12Z","snapshot_observed_at":"2026-08-06T23:53:22.457977Z","submitted_at":"2025-08-22T07:25:30Z","title":"Memento: Fine-tuning LLM Agents without Fine-tuning LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.16153","snapshot_observed_at":"2026-08-07T19:53:26.038845Z","title":"arXiv preprint arXiv:2508.16153 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:26.038845Z"},"links":{"cited_paper":"/paper/2508.16153","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:d423ab93ed9c316b266daca8a840ccaf33cfce26bce702ade987f9d6dd028f52","observation_id":"81cb3325-a230-4c30-89de-3c6bf51209f1","resolution":{"observed_at":"2026-08-07T19:53:26.038845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04141","last_updated":"2025-05-29T08:04:32Z","snapshot_observed_at":"2026-07-06T20:02:08.313625Z","submitted_at":"2024-12-05T13:10:54Z","title":"Reducing Tool Hallucination via Reliability Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04141","snapshot_observed_at":"2026-08-07T19:53:26.084902Z","title":"arXiv preprint arXiv:2412.04141 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:26.084902Z"},"links":{"cited_paper":"/paper/2412.04141","citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:68f416a276938018820b7d8cb05ad0a4a548ece197c2065d8faa3ba11f7fff19","observation_id":"2972291e-fd12-47ea-83e2-d0fd5007ea01","resolution":{"observed_at":"2026-08-07T19:53:26.084902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:26.141889Z","title":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:26.141889Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:1ee4330b3a10e987dd0d9b8762500b0cacf31948c8e84085df94253811c591fb","observation_id":"d28bd039-8e3e-45fc-a621-9b7d51d947a0","resolution":{"observed_at":"2026-08-07T19:53:26.141889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:53:26.173725Z","title":"arXiv preprint arXiv:2509.11543 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-07T19:53:26.173725Z"},"links":{"citing_paper":"/paper/2608.05987"},"observation_digest":"sha256:c3544d3611bc8ccf5c3e03c226b69ebb06d8d33d09d699125df9737537ad4745","observation_id":"6f511aa7-b0b0-485b-993e-0790a83398b0","resolution":{"observed_at":"2026-08-07T19:53:26.173725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.05987","last_updated":"2026-08-06T13:00:59Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T08:13:07.489160Z","submitted_at":"2026-08-06T13:00:59Z","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":58,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":77},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2608.05987."}